Data‐driven approaches to information access

This paper summarizes three lines of research that are motivated by the practical problem of helping users find information from external data sources, most notably computers. The application areas include information retrieval, text categorization, and question answering. A common theme in these applications is that practical information access problems can be solved by analyzing the statistical properties of words in large volumes of real world texts. The same statistical properties constrain human performance, thus we believe that solutions to practical information access problems can shed light on human knowledge representation and reasoning.     

Giving meaning to illness: An investigation of self-defining memories in patients with relapsing-remitting multiple sclerosis patients

Multiple sclerosis (MS) patients are often unable to adequately fulfill their established roles due to physical disabilities and cognitive changes, making this chronic illness particularly threatening to personal identity. Twenty-five MS patients and 25 healthy controls were asked to recall five self-defining memories (SDM). Overall characteristics of SDM did not differ between patients and controls; MS patients displayed preserved capacity to draw meaning upon past events. Moreover, almost two-thirds of MS patients mentioned at least one illness related SDM and about 25% of patients’ SDM referred to MS. These memories were experienced as more negative and associated with more tension than other SDM but led toward more positive emotion and less negative emotion over time; they were also more central and more integrated to the personal identity. We concluded that self-challenging events due to MS may trigger both cognitive and emotional processes enabling the integration of illness in patients’ self-representations.     

Oxytocin attenuates racial categorization in 14-month-old infants

Intergroup bias – the preferential attitudes one holds towards one's social group – is a ubiquitous socio-cognitive phenomenon. In fact, studies show that already in the first months of life, infants manifest a preference for members of their own social group. This points to the possibility of inborn mechanisms involved in social group cognition. Here we assess the effect of a biological activation of infants’ affiliative motivation on their social categorization capacity. In a first visit to the lab, mothers self-administered either Oxytocin (OT) or placebo (PL) via a nasal spray and then engaged in a face-to-face interaction with their 14-month-old infants, a procedure previously shown to increase OT levels in infants. Infants then performed a racial categorization task presented on an eye-tracker. Mothers and infants returned a week later and repeated the procedure while self-administering the complementary substance (i.e., PL or OT, respectively). In total, 24 infants completed the two visits. We found that whereas infants in the PL condition on the first visit exhibited racial categorization, infants in the OT condition in their first visit did not. Moreover, these patterns remained a week later despite the change in substance. Thus, OT inhibited racial categorization when infants first encountered the to-be-categorized faces. These findings highlight the role of affiliative motivation in social categorization, and suggest that the neurobiology of affiliation may provide insights on mechanisms that may be involved in the downstream prejudicial consequences of intergroup bias.     

L’approche existentielle dans l’accompagnement de personnes au chômage en perte de repères : les processus de construction du sens de la vie et du travail (études de cas)

The guidance design for unemployed people in changing career must increasingly take into account their existential concerns about career meaning. The literature about life and work meaning has highlighted the multifactorial complexity of meaning definition and determinants of meaning. This fact does not facilitate their use in counselling design. Bernaud et al. (2020) “Guidance for life and work meaning” device takes into account this problem; it offers many thinking prospects that organize meaning questioning in order to help people who loosed professional and personal markers. By analyzing an application of this device with the multiple cases method, our article examines how a meaning guidance helps unemployed people in giving a new direction of their career. The study of five cases with chronologic analysis unities shows how various meaning dimensions help people to clarify, to enrich and to transform their initial problematic during accompaniment and how they progress with different rhythms to major personal and vocational decisions.     

The factor structure of the Multiple Affect Adjective Check List-Revised (MAACL-R): Some statistical considerations

In response to Zuckerman's comments on my factor analyses of the MAACL-R, I focus on the statistical issues of the cutoff criterion for factor loadings and the evidence for the discriminant validity of the five-factor solution. Based upon the conclusions I draw in these two areas, I recommend that researchers either (a) use the two factor solution to the MAACL-R represented in the summary scores of Dysphoria and Positive Affect + Sensation Seeking or (b) include all MAACL-R scales in their studies and analyze their data to determine whether it is statistically appropriate to report results from a single scale (A, D, H, PA, or SS) rather than from the summary scores.     

Immediate and long-term retention of basic-level categories in 24-month-olds

This study assessed the effect of presenting single versus multiple exemplars of basic-level categories on 24-month-olds' long-term memory for categorical information. Sixty-four infants were tested in a paired-comparison recognition memory paradigm immediately and one week after familiarization with basic-level categories. Infants were randomly assigned to one of two familiarization conditions and one of two test conditions. In one familiarization condition (Varied Exemplar), they briefly viewed four different exemplars of each of 16 basic-level categories. In the other familiarization condition (Single Exemplar), they viewed only one instance of each category on four different trials. Test trials consisted of either an exemplar seen during familiarization (Familiar-Exemplar Test) or novel intracategory exemplar (Unfamiliar-Exemplar Test) paired with a stimulus from a novel category. Both immediately following familiarization and one week later, infants looked longer at test stimuli from the novel category irrespective of familiarization or test condition.     

Cognitive control models of multiple access IoT networks using LoRa technology

In this paper,we propose a random-access model for describing several wireless communication technologies. These networks have found application in the construction of wireless sensor networks, and the proposed model can be used for flows with different properties, considering the corresponding distribution functions. The model considers the technical features of the LoRa technology and subscriber traffic. We also address the management of random multiple wireless access in a Software-Defined Networking (SDN) like control architectures, and proposing a model for flows with different properties, considering the corresponding distribution functions. We develop a method for optimizing the parameters of an access network by the probability of data delivery. Then we describe the probability of bit error, frame loss, collision, and the choice of network parameters considering the heterogeneity of conditions for different users. Numerical results show the efficiency of our proposed scheme by maintaining the required network parameters in case of its function conditions changing.     

Selecting a new technology strategy using multiattribute,multilevel decision trees

Research indicates that selecting a strategy to best exploit a new technology is a complex decision-making process. The task involves making a series of decisions with multiple alternatives, each to be evaluated by multiple criteria whose values have high levels of uncertainty. This paper presents a methodology for modelling a new technology decision using decision trees and an optimizing algorithm. A problem of a mining company considering the adoption of new technology is used to illustrate the decision-making task and modelling methodology. A numerical solution to the case demonstrates the potential of the optimizing technique in strategy selection.     

A multi-objective mathematical programming problem with fuzzy relation constraints

In this study we discuss the problem of multi-objective mathematical programming with constraints defined by ‘max-min’ composite fuzzy relation equations. Since the feasible region is normally non-convex, the properties of the efficient points of a non-convex feasible region under multi-objectives are investigated and illustrated by examples. The necessary and sufficient conditions are proposed and proved. To facilitate decisions, a procedure that transforms these efficient points of an interval-valued decision space into a constant-valued decision space is proposed when the level of confidence is given by a decision maker. Then the transformed problem becomes a multi-attribute decision problem that can be evaluated by Yager's method to find the optimal alternative.